An Effective Taguchi Design For Parameter Tuning in Ant Colony Optimization Applied to the Capacitated Hub Location Problem

The parameter setting is a critical and computationally challenging issue for the successful implementation of the ant colony optimization (ACO) algorithm. This study proposes an efficient experimental design method using the Taguchi method for parameter optimization of ACO applied to the capacitated hub location problem. Four key parameters including the ant colony size, evaporation rate, selective preferences, and stopping condition are designated as design factors. The total transportation cost is adopted as the performance characteristic, while the number of potential hub nodes, the number of hubs, and the number of customers served are considered noise factors to simulate diverse problem environments. A robust design experiment utilizing inner and outer orthogonal arrays is conducted via computer simulation to determine the optimal parameter setting. The validity of this optimal setting is then confirmed by comparing its signal-to-noise ratios with those obtained from a full factorial design experiment.

Ant colony optimization, Parameter optimization, Taguchi method, Hub location problem

1. Dorigo, M. and Stützle, T., Ant Colony Optimization. (2000), MIT Press, Boston. [Google Scholar] [Crossref]

2. Schaffer, J. D., Caruana, R. A., Eshelman, L. J., and Das, R., A study of control parameter affecting online performance of genetic algorithms for function optimization. Proceedings of the 3rd International Conference on Genetic Algorithms, (1989), Arlington, 51-60. [Google Scholar] [Crossref]

3. Pakath, R. and Zaveri, J. S., Specifying critical inputs in a genetic algorithm-driven decision support system: an automated facility. Decision Science, 26 (1995), 749-771. [Google Scholar] [Crossref]

4. Gupta, M, Gupta, Y., and Kumar, A. (1993), Minimizing flow time variance in single machine system using genetic algorithms. European Journal of Operations Research, 70 (1993), 289-303. [Google Scholar] [Crossref]

5. Hsieh, C., Chou, J., and Wu, Y., Taguchi-MHGA method for optimizing grey-fuzzy gain-scheduler. Proceedings of the 6th International Conference on Automation Technology, (2000), Taiwan, 575-582. [Google Scholar] [Crossref]

6. Leung, Y. and Wang, Y., An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation, 5 (2001), 41-53. [Google Scholar] [Crossref]

7. Liu, T., Tsai, J., and Chou, J., Improved genetic algorithm for the job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 27 (2006), 1021-1029. [Google Scholar] [Crossref]

8. Phadke, M. S., Quality engineering using robust design. (1989), Prentice-Hall International. [Google Scholar] [Crossref]

9. Elhedhli, S. and Hu, F. X., Hub-and-spoke network design with congestion. Computers & Operations Research, 32 (2005), 1615-1632. [Google Scholar] [Crossref]

10. Costa, M. G., Captivo, M. E., and Climaco, J., Capacitated single allocation hub location problem - A bi-criteria approach. Computers & Operations Research, 35 (2008), 3671-3695. [Google Scholar] [Crossref]

11. Randall, M., Solution approaches for the capacitated single allocation hub location problem using ant colony optimization. Computational Optimization and Applications, 39 (2008), 239-261. [Google Scholar] [Crossref]

• The Impact Of UI/UX Design on User Trust and Task Completion in Civic Tech Platforms
• Solar Cell Photovoltaic Model Shell Sp 75
• Development of an Intelligent Traffic Management System to Address Visibility Obstruction at Urban Intersections: A Case Study of Ibadan Metropolis
• Optimum Placement of Facts Devices on an Interconnected Power Systems Using Particle Swarm Optimisation Technique
• Assessing Construction Transformation and Implication on Future Production Flow System